Comparison of Machine Learning methods applied to the estimation of manufacturing cost of jet engine components

This paper compares the performance of five statistical models on the estimation of manufacturing cost of jet engine components, during the early design phase and using real industrial data. The analysis shows that recent techniques such as Gradient Boosted Trees and Support Vector Regression are up to two times more efficient than the ones typically encountered in the literature (Multiple Linear Regression and Artificial Neural Networks). If goodness-of-fit and predictive accuracy remain crucial to assess the performance of a model, other criteria such as computational cost, easiness to train or interpretability should be considered when selecting a statistical method for estimating the manufacturing cost of mechanical parts. Ideally, cost estimators should rely on several statistical models concurrently, as their distinct characteristics yield complementary views on the drivers of manufacturing cost. Finally, some engineering insights revealed by the statistical analysis are presented. They include the ranking and quantification of the most important cost drivers, the approximation of the economic production function of component cost according to accumulated production volume and a different view on the traditional breakdown of manufacturing cost of some jet engine components. As a conclusion, Machine Learning appears to be an effective, affordable, accurate and scalable technique to cost mechanical parts in the early stage of the design process.